A reconfigurable real-time morphological system for augmented vision

There is a significant number of visually impaired individuals who suffer sensitivity loss to high spatial frequencies, for whom current optical devices are limited in degree of visual aid and practical application. Digital image and video processing offers a variety of effective visual enhancement methods that can be utilised to obtain a practical augmented vision head-mounted display device. The high spatial frequencies of an image can be extracted by edge detection techniques and overlaid on top of the original image to improve visual perception among the visually impaired. Augmented visual aid devices require highly user-customisable algorithm designs for subjective configuration per task, where current digital image processing visual aids offer very little user-configurable options. This paper presents a highly user-reconfigurable morphological edge enhancement system on field-programmable gate array, where the morphological, internal and external edge gradients can be selected from the presented architecture with specified edge thickness and magnitude. In addition, the morphology architecture supports reconfigurable shape structuring elements and configurable morphological operations. The proposed morphology-based visual enhancement system introduces a high degree of user flexibility in addition to meeting real-time constraints capable of obtaining 93 fps for high-definition image resolution

Fast recursive grayscale morphology operators: from the algorithm to the pipeline architecture

International audienceThis paper presents a new algorithm for an efficient computation of morphological operations for gray images and its specific hardware. The method is based on a new recursive morphological decomposition method of 8-convex structuring elements by only causal two-pixel structuring elements (2PSE). Whatever the element size, erosion or/and dilation can then be performed during a unique raster-like image scan involving a fixed reduced analysis neighborhood. The resulting process offers low computation complexity combined with easy description of the element form. The dedicated hardware is generic and fully regular, built from elementary interconnected stages. It has been synthesized into an FPGA and achieves high frequency performances for any shape and size of structuring element

Tracking Dynamic Features in Image Sequences.

This dissertation deals with detecting and tracking dynamic features in image sequences using digital image analysis algorithms. The tracking problem is complicated in oceanographic images due to the dynamic nature of the features. Specifically, the features of interest move, change size and shape. In the first part of the dissertation, the design and development of a new segmentation algorithm, Histogram-based Morphological Edge Detector (HMED), is presented. Mathematical morphology has been used in the past to develop efficient and robust edge detectors. But these morphological edge detectors do not extract weak gradient edge pixels, and they introduce spurious edge pixels. The primary reason for this is due to the fact that the morphological operations are defined in the domain of a pixel\u27s neighborhood. HMED defines new operations, namely H-dilation and H-erosion, which are defined in the domain of the histogram of the pixel\u27s neighborhood. The motivation for incorporating the histogram into the dilation and erosion is primarily due to the rich information content in the histogram compared to the one available in the pixel\u27s neighborhood. As a result, HMED extracts weak gradient pixels while suppressing the spurious edge pixels. An extensive comparison of all morphological edge detectors in the context of oceanographic digital images is also presented. In the second part of the dissertation, a new augmented region and edge segmentation technique for the interpretation of oceanographic features present in the AVHRR image is presented. The augmented technique uses a topography-based method that extracts topolographical labels such as concave, convex and flat pixels from the image. In this technique, first a bicubic polynomial is fitted to a pixel and its neighborhood, and topolographical label is assigned based on the first and second directional derivatives of the polynomial surface. Second, these labeled pixels are grouped and assembled into edges and regions. The augmented technique blends the edge and region information on a proximity based criterion to detect the features. A number of experimental results are also provided to show the significant improvement in tracking the features using the augmented technique over other previously designed techniques

Efficient Hardware Architectures and Algorithms for Embedded Vision Systems

PrésentationAbstract : In this talk we develop three main axes i) design of efficient hardware architectures, ii) computational efficient algorithms targeted for embedded vision systems and iii) hardware support for self-aware computing.We will introduce recent advances within the unifying framework of mathematical morphology. We propose a first morphological processor with arbitrarily large neighborhoods. It allows to obtain previously unachieved performances for serially composed morphological filters, geodesical and conditional operators. The cited processor is based on a novel algorithm formulation of morphological dilation. Finally, the applicative domain will be illustrated in scene understanding context for self aware embedded computing

Hardware architectures for infrared pedestrian detection systems

Infrared pedestrian detection systems struggle with real-time processing performance. Known solutions are limited to either low resolution systems with basic functionality running at full frame rate, or software based techniques featuring higher detection rates with full set of features, however running only in off-line mode for statistical analysis. Here, a comprehensive solution for real-time pedestrian detection is described. This research project includes investigation of possible solutions, design, development and implementation of a pedestrian detection system, processing data from infrared video source in real-time. Design requirements include processing at full frame rate as well as low memory and system resource consumption. The memory utilization is one of the major concerns since high demand for memory resources is a critical aspect in most image processing applications. For the purpose of this task, a number of general purpose image processing techniques were revised, taking into consideration the suitability for infrared pedestrian detection. These tasks include background separation, acquisition noise removal and object detection through connected component labelling. They are discussed and addressed in individual chapters. Various techniques for background segmentation are discussed. A chronological review of popular techniques is provided. The proposed architecture for background subtraction is based on selective running average for adaptive background model, supported by adaptive thresholding based on histogram calculation. In order to remove acquisition noise, a dual decomposed architecture was introduced, based on mathematical morphology and basic set theory definitions. It includes both erosion and dilation performed in a pipeline. For the purpose of object detection and feature extraction, a connected component labelling technique was employed, based on a single pass approach to fulfil real-time processing requirement. The system was implemented, verified and tested on XUP FPGA Development Board with Virtex-II Pro XC2VP30 chip from Xilinx. Details and limitation of the specific implementation are discussed. An overview of experimental pedestrian detection results is provided. The thesis concludes with system analysis and suggestions for future work

Resource Efficient Hardware Architecture for Fast Computation of Running Max/Min Filters

Running max/min filters on rectangular kernels are widely used in many digital signal and image processing applications. Filtering with a k×k kernel requires of k2−1 comparisons per sample for a direct implementation; thus, performance scales expensively with the kernel size k. Faster computations can be achieved by kernel decomposition and using constant time one-dimensional algorithms on custom hardware. This paper presents a hardware architecture for real-time computation of running max/min filters based on the van Herk/Gil-Werman (HGW) algorithm. The proposed architecture design uses less computation and memory resources than previously reported architectures when targeted to Field Programmable Gate Array (FPGA) devices. Implementation results show that the architecture is able to compute max/min filters, on 1024×1024 images with up to 255×255 kernels, in around 8.4 milliseconds, 120 frames per second, at a clock frequency of 250 MHz. The implementation is highly scalable for the kernel size with good performance/area tradeoff suitable for embedded applications. The applicability of the architecture is shown for local adaptive image thresholding

Development of novel gearbox lubrication condition monitoring sensors in the context of wind turbine gearboxes

Wind power has become established as an alternative power source that forms a significant proportion of national energy generation. An increasing proportion of turbines is being constructed offshore to exploit higher average wind speeds and to avoid development issues associated with onshore wind farms. Isolated locations and unpredictable weather conditions lead to increased access costs for operators when conducting scheduled and unscheduled maintenance and repairs. This has increased interest in condition monitoring systems which can track the current state of components within a wind turbine and provide operators with predicted future trends. Asset management can be improved through condition based maintenance regimes and preventative repairs. Development of novel condition monitoring systems that can accurately predict incipient damage can optimise operational performance and reduce the overall level of wind turbine generation costs. The work described in this thesis presents the development of novel sensors that may be applied to monitor wind turbine gearboxes, a component that experiences relatively high failure rates and causes considerable turbine downtime. Current systems and technology that may be adapted for use in wind turbine condition monitoring are evaluated. Lubrication related monitoring systems have been identified as an area that could be improved and are divided into those that track liberated wear material suspended in the lubricant and those that assess the state of the lubricant itself. This study presents two novel lubrication based gearbox monitoring sensors that potentially offer a low cost solution for continuous data capture. The first demonstrates the potential for active pixel sensors such as those found in digital cameras to capture images of wear particles within gearbox lubricants. Particle morphology was tracked in this system, allowing the type of particles to be correlated with the type of wear that is generated and a potential source. The second sensor uses a targeted form of infra-red absorption spectroscopy to track changes in the lubricant chemistry due to the increase in acidity. Ensuring the lubricant is functioning correctly decreases component stress and fatigue, reducing maintenance requirements.Wind power has become established as an alternative power source that forms a significant proportion of national energy generation. An increasing proportion of turbines is being constructed offshore to exploit higher average wind speeds and to avoid development issues associated with onshore wind farms. Isolated locations and unpredictable weather conditions lead to increased access costs for operators when conducting scheduled and unscheduled maintenance and repairs. This has increased interest in condition monitoring systems which can track the current state of components within a wind turbine and provide operators with predicted future trends. Asset management can be improved through condition based maintenance regimes and preventative repairs. Development of novel condition monitoring systems that can accurately predict incipient damage can optimise operational performance and reduce the overall level of wind turbine generation costs. The work described in this thesis presents the development of novel sensors that may be applied to monitor wind turbine gearboxes, a component that experiences relatively high failure rates and causes considerable turbine downtime. Current systems and technology that may be adapted for use in wind turbine condition monitoring are evaluated. Lubrication related monitoring systems have been identified as an area that could be improved and are divided into those that track liberated wear material suspended in the lubricant and those that assess the state of the lubricant itself. This study presents two novel lubrication based gearbox monitoring sensors that potentially offer a low cost solution for continuous data capture. The first demonstrates the potential for active pixel sensors such as those found in digital cameras to capture images of wear particles within gearbox lubricants. Particle morphology was tracked in this system, allowing the type of particles to be correlated with the type of wear that is generated and a potential source. The second sensor uses a targeted form of infra-red absorption spectroscopy to track changes in the lubricant chemistry due to the increase in acidity. Ensuring the lubricant is functioning correctly decreases component stress and fatigue, reducing maintenance requirements